Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations8000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory160.0 B

Variable types

Numeric14
Categorical6

Alerts

AdvertisingPlatform has constant value "IsConfid" Constant
AdvertisingTool has constant value "ToolConfid" Constant
CustomerID is uniformly distributed Uniform
CustomerID has unique values Unique
AdSpend has unique values Unique
ClickThroughRate has unique values Unique
ConversionRate has unique values Unique
PagesPerVisit has unique values Unique
TimeOnSite has unique values Unique
WebsiteVisits has 149 (1.9%) zeros Zeros
SocialShares has 96 (1.2%) zeros Zeros
EmailOpens has 403 (5.0%) zeros Zeros
EmailClicks has 794 (9.9%) zeros Zeros
PreviousPurchases has 838 (10.5%) zeros Zeros

Reproduction

Analysis started2025-03-09 19:27:56.640076
Analysis finished2025-03-09 19:28:16.919171
Duration20.28 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

CustomerID
Real number (ℝ)

Uniform  Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11999.5
Minimum8000
Maximum15999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:17.041811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile8399.95
Q19999.75
median11999.5
Q313999.25
95-th percentile15599.05
Maximum15999
Range7999
Interquartile range (IQR)3999.5

Descriptive statistics

Standard deviation2309.5454
Coefficient of variation (CV)0.19247014
Kurtosis-1.2
Mean11999.5
Median Absolute Deviation (MAD)2000
Skewness0
Sum95996000
Variance5334000
MonotonicityStrictly increasing
2025-03-10T00:58:17.209135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 1
 
< 0.1%
13329 1
 
< 0.1%
13342 1
 
< 0.1%
13341 1
 
< 0.1%
13340 1
 
< 0.1%
13339 1
 
< 0.1%
13338 1
 
< 0.1%
13337 1
 
< 0.1%
13336 1
 
< 0.1%
13335 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
8000 1
< 0.1%
8001 1
< 0.1%
8002 1
< 0.1%
8003 1
< 0.1%
8004 1
< 0.1%
8005 1
< 0.1%
8006 1
< 0.1%
8007 1
< 0.1%
8008 1
< 0.1%
8009 1
< 0.1%
ValueCountFrequency (%)
15999 1
< 0.1%
15998 1
< 0.1%
15997 1
< 0.1%
15996 1
< 0.1%
15995 1
< 0.1%
15994 1
< 0.1%
15993 1
< 0.1%
15992 1
< 0.1%
15991 1
< 0.1%
15990 1
< 0.1%

Age
Real number (ℝ)

Distinct52
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.6255
Minimum18
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:17.356474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q131
median43
Q356
95-th percentile67
Maximum69
Range51
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.902785
Coefficient of variation (CV)0.34160721
Kurtosis-1.1789944
Mean43.6255
Median Absolute Deviation (MAD)13
Skewness-0.0049164596
Sum349004
Variance222.09301
MonotonicityNot monotonic
2025-03-10T00:58:17.481635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 187
 
2.3%
62 182
 
2.3%
43 178
 
2.2%
66 178
 
2.2%
34 177
 
2.2%
49 173
 
2.2%
45 173
 
2.2%
52 172
 
2.1%
40 170
 
2.1%
42 167
 
2.1%
Other values (42) 6243
78.0%
ValueCountFrequency (%)
18 148
1.8%
19 150
1.9%
20 151
1.9%
21 160
2.0%
22 139
1.7%
23 154
1.9%
24 129
1.6%
25 162
2.0%
26 143
1.8%
27 136
1.7%
ValueCountFrequency (%)
69 147
1.8%
68 155
1.9%
67 137
1.7%
66 178
2.2%
65 137
1.7%
64 187
2.3%
63 140
1.8%
62 182
2.3%
61 150
1.9%
60 139
1.7%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
Female
4839 
Male
3161 

Length

Max length6
Median length6
Mean length5.20975
Min length4

Characters and Unicode

Total characters41678
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 4839
60.5%
Male 3161
39.5%

Length

2025-03-10T00:58:17.631810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:17.748220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 4839
60.5%
male 3161
39.5%

Most occurring characters

ValueCountFrequency (%)
e 12839
30.8%
a 8000
19.2%
l 8000
19.2%
F 4839
 
11.6%
m 4839
 
11.6%
M 3161
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 33678
80.8%
Uppercase Letter 8000
 
19.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12839
38.1%
a 8000
23.8%
l 8000
23.8%
m 4839
 
14.4%
Uppercase Letter
ValueCountFrequency (%)
F 4839
60.5%
M 3161
39.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 41678
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12839
30.8%
a 8000
19.2%
l 8000
19.2%
F 4839
 
11.6%
m 4839
 
11.6%
M 3161
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12839
30.8%
a 8000
19.2%
l 8000
19.2%
F 4839
 
11.6%
m 4839
 
11.6%
M 3161
 
7.6%

Income
Real number (ℝ)

Distinct7789
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84664.197
Minimum20014
Maximum149986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:17.877986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20014
5-th percentile26306.95
Q151744.5
median84926.5
Q3116815.75
95-th percentile143101.05
Maximum149986
Range129972
Interquartile range (IQR)65071.25

Descriptive statistics

Standard deviation37580.388
Coefficient of variation (CV)0.4438758
Kurtosis-1.2089562
Mean84664.197
Median Absolute Deviation (MAD)32454
Skewness-0.011298024
Sum6.7731357 × 108
Variance1.4122856 × 109
MonotonicityNot monotonic
2025-03-10T00:58:18.028107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50268 3
 
< 0.1%
73096 3
 
< 0.1%
119233 3
 
< 0.1%
142982 2
 
< 0.1%
146718 2
 
< 0.1%
51280 2
 
< 0.1%
49400 2
 
< 0.1%
46570 2
 
< 0.1%
60309 2
 
< 0.1%
107508 2
 
< 0.1%
Other values (7779) 7977
99.7%
ValueCountFrequency (%)
20014 1
< 0.1%
20018 1
< 0.1%
20029 1
< 0.1%
20042 1
< 0.1%
20051 1
< 0.1%
20057 1
< 0.1%
20059 1
< 0.1%
20068 1
< 0.1%
20079 1
< 0.1%
20123 1
< 0.1%
ValueCountFrequency (%)
149986 1
< 0.1%
149985 1
< 0.1%
149974 1
< 0.1%
149895 1
< 0.1%
149884 1
< 0.1%
149883 1
< 0.1%
149849 1
< 0.1%
149844 1
< 0.1%
149832 1
< 0.1%
149831 1
< 0.1%

CampaignChannel
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
Referral
1719 
PPC
1655 
Email
1557 
SEO
1550 
Social Media
1519 

Length

Max length12
Median length8
Mean length6.1725
Min length3

Characters and Unicode

Total characters49380
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSocial Media
2nd rowEmail
3rd rowPPC
4th rowPPC
5th rowPPC

Common Values

ValueCountFrequency (%)
Referral 1719
21.5%
PPC 1655
20.7%
Email 1557
19.5%
SEO 1550
19.4%
Social Media 1519
19.0%

Length

2025-03-10T00:58:18.161175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:18.268316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
referral 1719
18.1%
ppc 1655
17.4%
email 1557
16.4%
seo 1550
16.3%
social 1519
16.0%
media 1519
16.0%

Most occurring characters

ValueCountFrequency (%)
a 6314
12.8%
e 4957
10.0%
l 4795
 
9.7%
i 4595
 
9.3%
r 3438
 
7.0%
P 3310
 
6.7%
E 3107
 
6.3%
S 3069
 
6.2%
R 1719
 
3.5%
f 1719
 
3.5%
Other values (8) 12357
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31932
64.7%
Uppercase Letter 15929
32.3%
Space Separator 1519
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 6314
19.8%
e 4957
15.5%
l 4795
15.0%
i 4595
14.4%
r 3438
10.8%
f 1719
 
5.4%
m 1557
 
4.9%
o 1519
 
4.8%
c 1519
 
4.8%
d 1519
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
P 3310
20.8%
E 3107
19.5%
S 3069
19.3%
R 1719
10.8%
C 1655
10.4%
O 1550
9.7%
M 1519
9.5%
Space Separator
ValueCountFrequency (%)
1519
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47861
96.9%
Common 1519
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 6314
13.2%
e 4957
10.4%
l 4795
10.0%
i 4595
9.6%
r 3438
 
7.2%
P 3310
 
6.9%
E 3107
 
6.5%
S 3069
 
6.4%
R 1719
 
3.6%
f 1719
 
3.6%
Other values (7) 10838
22.6%
Common
ValueCountFrequency (%)
1519
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 6314
12.8%
e 4957
10.0%
l 4795
 
9.7%
i 4595
 
9.3%
r 3438
 
7.0%
P 3310
 
6.7%
E 3107
 
6.3%
S 3069
 
6.2%
R 1719
 
3.5%
f 1719
 
3.5%
Other values (8) 12357
25.0%

CampaignType
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
Conversion
2077 
Awareness
1988 
Consideration
1988 
Retention
1947 

Length

Max length13
Median length10
Mean length10.253625
Min length9

Characters and Unicode

Total characters82029
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAwareness
2nd rowRetention
3rd rowAwareness
4th rowConversion
5th rowConversion

Common Values

ValueCountFrequency (%)
Conversion 2077
26.0%
Awareness 1988
24.9%
Consideration 1988
24.9%
Retention 1947
24.3%

Length

2025-03-10T00:58:18.419065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:18.552537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
conversion 2077
26.0%
awareness 1988
24.9%
consideration 1988
24.9%
retention 1947
24.3%

Most occurring characters

ValueCountFrequency (%)
n 14012
17.1%
e 11935
14.5%
o 10077
12.3%
s 8041
9.8%
i 8000
9.8%
r 6053
7.4%
t 5882
7.2%
C 4065
 
5.0%
a 3976
 
4.8%
v 2077
 
2.5%
Other values (4) 7911
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74029
90.2%
Uppercase Letter 8000
 
9.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 14012
18.9%
e 11935
16.1%
o 10077
13.6%
s 8041
10.9%
i 8000
10.8%
r 6053
8.2%
t 5882
7.9%
a 3976
 
5.4%
v 2077
 
2.8%
w 1988
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 4065
50.8%
A 1988
24.9%
R 1947
24.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 82029
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 14012
17.1%
e 11935
14.5%
o 10077
12.3%
s 8041
9.8%
i 8000
9.8%
r 6053
7.4%
t 5882
7.2%
C 4065
 
5.0%
a 3976
 
4.8%
v 2077
 
2.5%
Other values (4) 7911
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 14012
17.1%
e 11935
14.5%
o 10077
12.3%
s 8041
9.8%
i 8000
9.8%
r 6053
7.4%
t 5882
7.2%
C 4065
 
5.0%
a 3976
 
4.8%
v 2077
 
2.5%
Other values (4) 7911
9.6%

AdSpend
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.9448
Minimum100.05481
Maximum9997.9148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:18.719998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100.05481
5-th percentile613.54475
Q12523.2212
median5013.44
Q37407.9894
95-th percentile9470.4932
Maximum9997.9148
Range9897.86
Interquartile range (IQR)4884.7682

Descriptive statistics

Standard deviation2838.0382
Coefficient of variation (CV)0.56750039
Kurtosis-1.1978015
Mean5000.9448
Median Absolute Deviation (MAD)2449.0595
Skewness0.019224264
Sum40007559
Variance8054460.6
MonotonicityNot monotonic
2025-03-10T00:58:18.881142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6497.870068 1
 
< 0.1%
4045.47167 1
 
< 0.1%
4690.02965 1
 
< 0.1%
6908.321602 1
 
< 0.1%
3573.890951 1
 
< 0.1%
3680.878565 1
 
< 0.1%
5774.073688 1
 
< 0.1%
763.6905159 1
 
< 0.1%
3979.781943 1
 
< 0.1%
6697.228227 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
100.0548131 1
< 0.1%
100.6682267 1
< 0.1%
100.9659387 1
< 0.1%
103.409243 1
< 0.1%
103.9569566 1
< 0.1%
108.0647114 1
< 0.1%
110.1686722 1
< 0.1%
112.3044986 1
< 0.1%
112.3762624 1
< 0.1%
114.5034314 1
< 0.1%
ValueCountFrequency (%)
9997.914781 1
< 0.1%
9997.347635 1
< 0.1%
9997.002376 1
< 0.1%
9996.986533 1
< 0.1%
9992.481744 1
< 0.1%
9989.800448 1
< 0.1%
9989.443997 1
< 0.1%
9988.281818 1
< 0.1%
9987.926017 1
< 0.1%
9986.858288 1
< 0.1%

ClickThroughRate
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15482865
Minimum0.010004854
Maximum0.29996826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:19.014435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.010004854
5-th percentile0.024457408
Q10.082634965
median0.15450549
Q30.22820696
95-th percentile0.2857305
Maximum0.29996826
Range0.28996341
Interquartile range (IQR)0.145572

Descriptive statistics

Standard deviation0.084007204
Coefficient of variation (CV)0.54258178
Kurtosis-1.2026601
Mean0.15482865
Median Absolute Deviation (MAD)0.072745599
Skewness0.011066343
Sum1238.6292
Variance0.0070572103
MonotonicityNot monotonic
2025-03-10T00:58:19.315670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04391851074 1
 
< 0.1%
0.02522762609 1
 
< 0.1%
0.1703320691 1
 
< 0.1%
0.2694749701 1
 
< 0.1%
0.1389667385 1
 
< 0.1%
0.0884045254 1
 
< 0.1%
0.2728871951 1
 
< 0.1%
0.05893755922 1
 
< 0.1%
0.1090679975 1
 
< 0.1%
0.06112656693 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
0.01000485351 1
< 0.1%
0.01005221131 1
< 0.1%
0.01017247983 1
< 0.1%
0.01018817218 1
< 0.1%
0.0102002376 1
< 0.1%
0.0102557008 1
< 0.1%
0.01035941897 1
< 0.1%
0.01037580517 1
< 0.1%
0.01039495751 1
< 0.1%
0.01040392491 1
< 0.1%
ValueCountFrequency (%)
0.2999682637 1
< 0.1%
0.2997879187 1
< 0.1%
0.2997618364 1
< 0.1%
0.2997418946 1
< 0.1%
0.2997339499 1
< 0.1%
0.2996629508 1
< 0.1%
0.2996396889 1
< 0.1%
0.2996373584 1
< 0.1%
0.2995940185 1
< 0.1%
0.2995574239 1
< 0.1%

ConversionRate
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10438874
Minimum0.010017783
Maximum0.19999471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:19.462750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.010017783
5-th percentile0.019154243
Q10.056409514
median0.10404646
Q30.1520769
95-th percentile0.18986731
Maximum0.19999471
Range0.18997693
Interquartile range (IQR)0.095667389

Descriptive statistics

Standard deviation0.054878303
Coefficient of variation (CV)0.52571096
Kurtosis-1.2110434
Mean0.10438874
Median Absolute Deviation (MAD)0.047838236
Skewness0.012777379
Sum835.10988
Variance0.0030116281
MonotonicityNot monotonic
2025-03-10T00:58:19.617568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08803141207 1
 
< 0.1%
0.1480552393 1
 
< 0.1%
0.1141119463 1
 
< 0.1%
0.1520735161 1
 
< 0.1%
0.1122285753 1
 
< 0.1%
0.1735192641 1
 
< 0.1%
0.1085024117 1
 
< 0.1%
0.03327698521 1
 
< 0.1%
0.1087816704 1
 
< 0.1%
0.1927050589 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
0.01001778283 1
< 0.1%
0.01001989505 1
< 0.1%
0.01005909238 1
< 0.1%
0.01009448986 1
< 0.1%
0.01010014616 1
< 0.1%
0.0101002903 1
< 0.1%
0.01010622784 1
< 0.1%
0.01011644982 1
< 0.1%
0.01011826941 1
< 0.1%
0.01013395225 1
< 0.1%
ValueCountFrequency (%)
0.199994708 1
< 0.1%
0.1999918213 1
< 0.1%
0.1999665125 1
< 0.1%
0.1999377382 1
< 0.1%
0.1998858877 1
< 0.1%
0.1998398506 1
< 0.1%
0.1997963503 1
< 0.1%
0.1997266159 1
< 0.1%
0.1997100001 1
< 0.1%
0.1996981396 1
< 0.1%

WebsiteVisits
Real number (ℝ)

Zeros 

Distinct50
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.751625
Minimum0
Maximum49
Zeros149
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:19.763377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median25
Q337
95-th percentile47
Maximum49
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.312269
Coefficient of variation (CV)0.57823552
Kurtosis-1.1886801
Mean24.751625
Median Absolute Deviation (MAD)12
Skewness-0.01713941
Sum198013
Variance204.84104
MonotonicityNot monotonic
2025-03-10T00:58:19.895327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 188
 
2.4%
43 188
 
2.4%
16 183
 
2.3%
44 181
 
2.3%
47 180
 
2.2%
32 176
 
2.2%
19 175
 
2.2%
35 173
 
2.2%
31 173
 
2.2%
33 171
 
2.1%
Other values (40) 6212
77.6%
ValueCountFrequency (%)
0 149
1.9%
1 151
1.9%
2 145
1.8%
3 149
1.9%
4 161
2.0%
5 142
1.8%
6 156
1.9%
7 166
2.1%
8 164
2.1%
9 151
1.9%
ValueCountFrequency (%)
49 140
1.8%
48 148
1.8%
47 180
2.2%
46 164
2.1%
45 161
2.0%
44 181
2.3%
43 188
2.4%
42 159
2.0%
41 155
1.9%
40 158
2.0%

PagesPerVisit
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5492992
Minimum1.0004279
Maximum9.9990554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:20.038301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0004279
5-th percentile1.4823117
Q13.3024789
median5.5342566
Q37.8357557
95-th percentile9.5811594
Maximum9.9990554
Range8.9986275
Interquartile range (IQR)4.5332768

Descriptive statistics

Standard deviation2.6073581
Coefficient of variation (CV)0.46985358
Kurtosis-1.2202266
Mean5.5492992
Median Absolute Deviation (MAD)2.2753009
Skewness-0.012509568
Sum44394.394
Variance6.7983162
MonotonicityNot monotonic
2025-03-10T00:58:20.164252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.399016528 1
 
< 0.1%
1.417457274 1
 
< 0.1%
4.793173065 1
 
< 0.1%
6.719928637 1
 
< 0.1%
2.641093646 1
 
< 0.1%
2.591519175 1
 
< 0.1%
9.534702639 1
 
< 0.1%
2.475834436 1
 
< 0.1%
6.874297019 1
 
< 0.1%
6.620711026 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
1.000427851 1
< 0.1%
1.001882437 1
< 0.1%
1.002427888 1
< 0.1%
1.00296447 1
< 0.1%
1.004400711 1
< 0.1%
1.006006728 1
< 0.1%
1.009495833 1
< 0.1%
1.010064086 1
< 0.1%
1.013655899 1
< 0.1%
1.014044091 1
< 0.1%
ValueCountFrequency (%)
9.999055371 1
< 0.1%
9.997133812 1
< 0.1%
9.995795389 1
< 0.1%
9.995262679 1
< 0.1%
9.989911711 1
< 0.1%
9.989524463 1
< 0.1%
9.989170355 1
< 0.1%
9.987093273 1
< 0.1%
9.986701607 1
< 0.1%
9.986205777 1
< 0.1%

TimeOnSite
Real number (ℝ)

Unique 

Distinct8000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7277182
Minimum0.50166908
Maximum14.995311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:20.288133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.50166908
5-th percentile1.1648409
Q14.0683396
median7.6829564
Q311.481468
95-th percentile14.307055
Maximum14.995311
Range14.493642
Interquartile range (IQR)7.4131285

Descriptive statistics

Standard deviation4.2282182
Coefficient of variation (CV)0.54714964
Kurtosis-1.2085483
Mean7.7277182
Median Absolute Deviation (MAD)3.7002448
Skewness0.01460969
Sum61821.745
Variance17.877829
MonotonicityNot monotonic
2025-03-10T00:58:20.414820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.396802581 1
 
< 0.1%
1.258320451 1
 
< 0.1%
14.16562964 1
 
< 0.1%
9.444860363 1
 
< 0.1%
9.197505704 1
 
< 0.1%
14.46674822 1
 
< 0.1%
11.8887485 1
 
< 0.1%
6.000367876 1
 
< 0.1%
3.203529014 1
 
< 0.1%
7.60677686 1
 
< 0.1%
Other values (7990) 7990
99.9%
ValueCountFrequency (%)
0.5016690842 1
< 0.1%
0.5016840269 1
< 0.1%
0.5021161212 1
< 0.1%
0.5068032521 1
< 0.1%
0.5100603022 1
< 0.1%
0.5102689885 1
< 0.1%
0.5117514828 1
< 0.1%
0.5136753714 1
< 0.1%
0.5196408518 1
< 0.1%
0.5226537649 1
< 0.1%
ValueCountFrequency (%)
14.99531141 1
< 0.1%
14.99502595 1
< 0.1%
14.99455053 1
< 0.1%
14.99279517 1
< 0.1%
14.9923475 1
< 0.1%
14.98835962 1
< 0.1%
14.98430349 1
< 0.1%
14.98371812 1
< 0.1%
14.98320475 1
< 0.1%
14.97930616 1
< 0.1%

SocialShares
Real number (ℝ)

Zeros 

Distinct100
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.79975
Minimum0
Maximum99
Zeros96
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:20.558433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q125
median50
Q375
95-th percentile95
Maximum99
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.901165
Coefficient of variation (CV)0.58034758
Kurtosis-1.2092647
Mean49.79975
Median Absolute Deviation (MAD)25
Skewness-0.011357203
Sum398398
Variance835.27731
MonotonicityNot monotonic
2025-03-10T00:58:20.704330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 100
 
1.2%
24 97
 
1.2%
0 96
 
1.2%
48 95
 
1.2%
33 95
 
1.2%
25 94
 
1.2%
66 93
 
1.2%
87 93
 
1.2%
74 93
 
1.2%
11 92
 
1.1%
Other values (90) 7052
88.1%
ValueCountFrequency (%)
0 96
1.2%
1 69
0.9%
2 79
1.0%
3 77
1.0%
4 80
1.0%
5 71
0.9%
6 73
0.9%
7 74
0.9%
8 79
1.0%
9 77
1.0%
ValueCountFrequency (%)
99 74
0.9%
98 73
0.9%
97 89
1.1%
96 88
1.1%
95 81
1.0%
94 87
1.1%
93 87
1.1%
92 73
0.9%
91 83
1.0%
90 73
0.9%

EmailOpens
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.476875
Minimum0
Maximum19
Zeros403
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:20.843881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median9
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7111113
Coefficient of variation (CV)0.60263656
Kurtosis-1.1701646
Mean9.476875
Median Absolute Deviation (MAD)5
Skewness0.007678838
Sum75815
Variance32.616792
MonotonicityNot monotonic
2025-03-10T00:58:20.966336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
9 433
 
5.4%
8 424
 
5.3%
7 419
 
5.2%
10 418
 
5.2%
11 411
 
5.1%
13 410
 
5.1%
4 407
 
5.1%
5 404
 
5.1%
18 404
 
5.1%
0 403
 
5.0%
Other values (10) 3867
48.3%
ValueCountFrequency (%)
0 403
5.0%
1 395
4.9%
2 368
4.6%
3 386
4.8%
4 407
5.1%
5 404
5.1%
6 394
4.9%
7 419
5.2%
8 424
5.3%
9 433
5.4%
ValueCountFrequency (%)
19 389
4.9%
18 404
5.1%
17 385
4.8%
16 369
4.6%
15 391
4.9%
14 402
5.0%
13 410
5.1%
12 388
4.9%
11 411
5.1%
10 418
5.2%

EmailClicks
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.467375
Minimum0
Maximum9
Zeros794
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:21.078883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8565636
Coefficient of variation (CV)0.63942776
Kurtosis-1.2072658
Mean4.467375
Median Absolute Deviation (MAD)2
Skewness0.023365585
Sum35739
Variance8.1599556
MonotonicityNot monotonic
2025-03-10T00:58:21.166764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 844
10.5%
3 828
10.3%
2 821
10.3%
7 810
10.1%
9 794
9.9%
0 794
9.9%
1 791
9.9%
6 787
9.8%
5 780
9.8%
8 751
9.4%
ValueCountFrequency (%)
0 794
9.9%
1 791
9.9%
2 821
10.3%
3 828
10.3%
4 844
10.5%
5 780
9.8%
6 787
9.8%
7 810
10.1%
8 751
9.4%
9 794
9.9%
ValueCountFrequency (%)
9 794
9.9%
8 751
9.4%
7 810
10.1%
6 787
9.8%
5 780
9.8%
4 844
10.5%
3 828
10.3%
2 821
10.3%
1 791
9.9%
0 794
9.9%

PreviousPurchases
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4855
Minimum0
Maximum9
Zeros838
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:21.258489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8880932
Coefficient of variation (CV)0.64387319
Kurtosis-1.2271848
Mean4.4855
Median Absolute Deviation (MAD)3
Skewness0.0057118049
Sum35884
Variance8.3410824
MonotonicityNot monotonic
2025-03-10T00:58:21.354356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 838
10.5%
3 822
10.3%
9 819
10.2%
6 818
10.2%
4 797
10.0%
8 796
10.0%
1 794
9.9%
5 779
9.7%
2 773
9.7%
7 764
9.6%
ValueCountFrequency (%)
0 838
10.5%
1 794
9.9%
2 773
9.7%
3 822
10.3%
4 797
10.0%
5 779
9.7%
6 818
10.2%
7 764
9.6%
8 796
10.0%
9 819
10.2%
ValueCountFrequency (%)
9 819
10.2%
8 796
10.0%
7 764
9.6%
6 818
10.2%
5 779
9.7%
4 797
10.0%
3 822
10.3%
2 773
9.7%
1 794
9.9%
0 838
10.5%

LoyaltyPoints
Real number (ℝ)

Distinct3983
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2490.2685
Minimum0
Maximum4999
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2025-03-10T00:58:21.459441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile260.95
Q11254.75
median2497
Q33702.25
95-th percentile4747
Maximum4999
Range4999
Interquartile range (IQR)2447.5

Descriptive statistics

Standard deviation1429.5272
Coefficient of variation (CV)0.57404539
Kurtosis-1.1748837
Mean2490.2685
Median Absolute Deviation (MAD)1222
Skewness0.01630347
Sum19922148
Variance2043547.9
MonotonicityNot monotonic
2025-03-10T00:58:21.594039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1747 7
 
0.1%
1392 7
 
0.1%
1593 6
 
0.1%
4687 6
 
0.1%
3814 6
 
0.1%
577 6
 
0.1%
687 6
 
0.1%
2662 6
 
0.1%
2091 6
 
0.1%
3055 6
 
0.1%
Other values (3973) 7938
99.2%
ValueCountFrequency (%)
0 4
0.1%
1 1
 
< 0.1%
3 2
< 0.1%
4 3
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
< 0.1%
10 2
< 0.1%
ValueCountFrequency (%)
4999 3
< 0.1%
4998 1
 
< 0.1%
4997 1
 
< 0.1%
4994 5
0.1%
4990 2
 
< 0.1%
4989 1
 
< 0.1%
4987 4
0.1%
4986 1
 
< 0.1%
4985 1
 
< 0.1%
4983 1
 
< 0.1%

AdvertisingPlatform
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
IsConfid
8000 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters64000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIsConfid
2nd rowIsConfid
3rd rowIsConfid
4th rowIsConfid
5th rowIsConfid

Common Values

ValueCountFrequency (%)
IsConfid 8000
100.0%

Length

2025-03-10T00:58:21.709733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:21.800390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
isconfid 8000
100.0%

Most occurring characters

ValueCountFrequency (%)
I 8000
12.5%
s 8000
12.5%
C 8000
12.5%
o 8000
12.5%
n 8000
12.5%
f 8000
12.5%
i 8000
12.5%
d 8000
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48000
75.0%
Uppercase Letter 16000
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 8000
16.7%
o 8000
16.7%
n 8000
16.7%
f 8000
16.7%
i 8000
16.7%
d 8000
16.7%
Uppercase Letter
ValueCountFrequency (%)
I 8000
50.0%
C 8000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 8000
12.5%
s 8000
12.5%
C 8000
12.5%
o 8000
12.5%
n 8000
12.5%
f 8000
12.5%
i 8000
12.5%
d 8000
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 8000
12.5%
s 8000
12.5%
C 8000
12.5%
o 8000
12.5%
n 8000
12.5%
f 8000
12.5%
i 8000
12.5%
d 8000
12.5%

AdvertisingTool
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
ToolConfid
8000 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters80000
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowToolConfid
2nd rowToolConfid
3rd rowToolConfid
4th rowToolConfid
5th rowToolConfid

Common Values

ValueCountFrequency (%)
ToolConfid 8000
100.0%

Length

2025-03-10T00:58:21.908835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:21.989204image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
toolconfid 8000
100.0%

Most occurring characters

ValueCountFrequency (%)
o 24000
30.0%
T 8000
 
10.0%
l 8000
 
10.0%
C 8000
 
10.0%
n 8000
 
10.0%
f 8000
 
10.0%
i 8000
 
10.0%
d 8000
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 64000
80.0%
Uppercase Letter 16000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 24000
37.5%
l 8000
 
12.5%
n 8000
 
12.5%
f 8000
 
12.5%
i 8000
 
12.5%
d 8000
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
T 8000
50.0%
C 8000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 24000
30.0%
T 8000
 
10.0%
l 8000
 
10.0%
C 8000
 
10.0%
n 8000
 
10.0%
f 8000
 
10.0%
i 8000
 
10.0%
d 8000
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 24000
30.0%
T 8000
 
10.0%
l 8000
 
10.0%
C 8000
 
10.0%
n 8000
 
10.0%
f 8000
 
10.0%
i 8000
 
10.0%
d 8000
 
10.0%

Conversion
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
1
7012 
0
988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Length

2025-03-10T00:58:22.100331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-10T00:58:22.190609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Most occurring characters

ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 8000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7012
87.6%
0 988
 
12.3%

Interactions

2025-03-10T00:58:15.165031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:57.769578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.172583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.463407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.712744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.137442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.467543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.918896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.223643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.446836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.719753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.972852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.391785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.924332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.270269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:57.924881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.264869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.557159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.830079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.248827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.571450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.039065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.320492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.545893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.815068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.085917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.519756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.019382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.356489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.019182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.441798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.642434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.932000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.339861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.659440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.131706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.408553image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.637798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.893794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.185861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.798737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.109869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.445514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.104542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.533354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.736461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.042229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.445900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.760615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.223727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.490488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.733581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.989249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.283200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.881845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.195616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.535919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.209684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.615790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.824019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.122809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.540618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.851544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.304766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.567113image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.813045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.083503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.372041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.974189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.284225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.629726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.296203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.696716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.917479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.226332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.649443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.951088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.402462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.668292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.906934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.168353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.467173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.053777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.363784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.728934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.402833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.791117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.005351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.321755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.750989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.048739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.496092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.755839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.007956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.269947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.571131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.157187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.459415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.815137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.487705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.878724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.100272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.417512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.843919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.137001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.580405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.842282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.097199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.357661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.661064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.246329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.544389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.896437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.583020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.958118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.179098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.517759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.934434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.224104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.660773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.916955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.177058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.440006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.764332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.325962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.655093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.997094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.680783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.038884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.259171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.610813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.016420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.319801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.760455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.008123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.273751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.530202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.859888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.423310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.740067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:16.082086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.770745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.129203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.355147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.720827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.117653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.414706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.843164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.093149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.362258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.614915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:11.955647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.513829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.830957image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:16.171618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.875618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.212041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.459178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.821800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.212249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.509718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:06.954229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.186424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.459684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.708930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.059586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.627044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:14.910517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:16.262041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:58.983035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.292691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.534567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:02.932265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.300712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.594843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.040811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.256736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.544885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.785606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.145984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.754848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.005901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:16.350712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:57:59.073087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:00.378792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:01.604037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:03.029959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:04.379883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:05.814682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:07.128825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:08.359596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:09.624410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:10.881273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:12.239158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:13.839074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-10T00:58:15.085376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-10T00:58:22.266751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AdSpendAgeCampaignChannelCampaignTypeClickThroughRateConversionConversionRateCustomerIDEmailClicksEmailOpensGenderIncomeLoyaltyPointsPagesPerVisitPreviousPurchasesSocialSharesTimeOnSiteWebsiteVisits
AdSpend1.000-0.0050.0000.010-0.0090.139-0.0200.0160.0010.0170.0000.0030.002-0.0090.002-0.021-0.0050.007
Age-0.0051.0000.0000.0000.0110.0290.020-0.0040.0090.0150.0000.0090.014-0.009-0.007-0.009-0.013-0.002
CampaignChannel0.0000.0001.0000.0000.0170.0000.0000.0160.0230.0040.0000.0140.0000.0130.0000.0140.0000.011
CampaignType0.0100.0000.0001.0000.0000.1010.0000.0000.0130.0000.0120.0000.0000.0050.0000.0000.0000.013
ClickThroughRate-0.0090.0110.0170.0001.0000.143-0.0080.005-0.011-0.0060.0150.008-0.0170.000-0.000-0.013-0.009-0.023
Conversion0.1390.0290.0000.1010.1431.0000.1360.3210.1470.1430.0000.0000.1290.1470.1730.0130.1470.123
ConversionRate-0.0200.0200.0000.000-0.0080.1361.0000.0100.0070.0060.0100.018-0.0010.019-0.0230.0090.008-0.012
CustomerID0.016-0.0040.0160.0000.0050.3210.0101.000-0.003-0.0030.0000.002-0.0240.000-0.013-0.012-0.014-0.000
EmailClicks0.0010.0090.0230.013-0.0110.1470.007-0.0031.0000.0010.0310.009-0.003-0.0000.0010.0030.0020.003
EmailOpens0.0170.0150.0040.000-0.0060.1430.006-0.0030.0011.0000.022-0.001-0.0030.0000.001-0.012-0.0050.006
Gender0.0000.0000.0000.0120.0150.0000.0100.0000.0310.0221.0000.0410.0000.0000.0000.0000.0330.000
Income0.0030.0090.0140.0000.0080.0000.0180.0020.009-0.0010.0411.000-0.0070.004-0.012-0.0060.019-0.003
LoyaltyPoints0.0020.0140.0000.000-0.0170.129-0.001-0.024-0.003-0.0030.000-0.0071.000-0.0130.012-0.005-0.0110.003
PagesPerVisit-0.009-0.0090.0130.0050.0000.1470.0190.000-0.0000.0000.0000.004-0.0131.000-0.0160.0080.017-0.012
PreviousPurchases0.002-0.0070.0000.000-0.0000.173-0.023-0.0130.0010.0010.000-0.0120.012-0.0161.000-0.013-0.0060.014
SocialShares-0.021-0.0090.0140.000-0.0130.0130.009-0.0120.003-0.0120.000-0.006-0.0050.008-0.0131.0000.0020.000
TimeOnSite-0.005-0.0130.0000.000-0.0090.1470.008-0.0140.002-0.0050.0330.019-0.0110.017-0.0060.0021.000-0.022
WebsiteVisits0.007-0.0020.0110.013-0.0230.123-0.012-0.0000.0030.0060.000-0.0030.003-0.0120.0140.000-0.0221.000

Missing values

2025-03-10T00:58:16.490739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-10T00:58:16.764930image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CustomerIDAgeGenderIncomeCampaignChannelCampaignTypeAdSpendClickThroughRateConversionRateWebsiteVisitsPagesPerVisitTimeOnSiteSocialSharesEmailOpensEmailClicksPreviousPurchasesLoyaltyPointsAdvertisingPlatformAdvertisingToolConversion
0800056Female136912Social MediaAwareness6497.8700680.0439190.08803102.3990177.39680319694688IsConfidToolConfid1
1800169Male41760EmailRetention3898.6686060.1557250.182725422.9171385.35254952723459IsConfidToolConfid1
2800246Female88456PPCAwareness1546.4295960.2774900.07642328.22361913.794901011282337IsConfidToolConfid1
3800332Female44085PPCConversion539.5259360.1376110.088004474.54093914.688363892202463IsConfidToolConfid1
4800460Female83964PPCConversion1678.0435730.2528510.10994002.04684713.99337066684345IsConfidToolConfid1
5800525Female42925Social MediaAwareness9579.3882470.1537950.16131662.1258507.752831955803316IsConfidToolConfid1
6800638Female25615ReferralAwareness7302.8998520.0409750.060977421.75399510.698672541436930IsConfidToolConfid1
7800756Female57083Social MediaConversion5324.2836670.0528780.188946482.6260152.987817969302983IsConfidToolConfid1
8800836Female140788EmailRetention9421.2509510.0235360.112585135.47284314.28742173485460IsConfidToolConfid1
9800940Male130764Social MediaAwareness6229.1933330.0666410.169786221.1356654.613312148483789IsConfidToolConfid1
CustomerIDAgeGenderIncomeCampaignChannelCampaignTypeAdSpendClickThroughRateConversionRateWebsiteVisitsPagesPerVisitTimeOnSiteSocialSharesEmailOpensEmailClicksPreviousPurchasesLoyaltyPointsAdvertisingPlatformAdvertisingToolConversion
79901599043Female84823Social MediaConsideration880.9052330.0709840.097500125.82801110.430608702422848IsConfidToolConfid1
79911599162Female69289SEORetention6118.8638810.2403930.047079392.2101426.735464246533075IsConfidToolConfid1
79921599237Male138422ReferralRetention2024.1488480.1001380.068403176.9870765.3078441417052733IsConfidToolConfid0
79931599320Male120271Social MediaRetention6973.1201740.1485890.174582347.5450466.454223812173066IsConfidToolConfid0
79941599469Female124883ReferralAwareness7875.3726330.2686370.08280506.0594419.221240101592448IsConfidToolConfid0
79951599521Male24849EmailAwareness8518.3085750.2437920.116773239.69360214.227794701367286IsConfidToolConfid0
79961599643Female44718SEORetention1424.6134460.2367400.190061499.4990103.5011065213151502IsConfidToolConfid0
79971599728Female125471ReferralConsideration4609.5346350.0565260.133826352.85324114.618323381603738IsConfidToolConfid1
79981599819Female107862PPCConsideration9476.1063540.0239610.138386491.0029643.876623861572709IsConfidToolConfid1
79991599931Female93002EmailAwareness7743.6270700.1856700.057228156.96473912.76366021899341IsConfidToolConfid0